1. Field of Invention
This invention relates to methods and systems for interactive classification of object.
2. Description of Related Art
“Sensemaking” is a process of gathering, understanding, and using information for a purpose. Sensemaking tasks often involve searching for relevant documents and then extracting and reformulating information so that the information can be better utilized. A sensemaker gathers information, identifies and extracts portions of the information, organizes such portions for efficient use, and ultimately incorporates the information in a work product with the required logical and rhetorical structure.
A common part of many sensemaking tasks is organizing “factoids” or other units of information, or objects, into related groups. Objects may be any form, such as simple text or a list of items. The difficulty of organizing objects depends on several practical factors, including the number of objects to be organized and the efficiency of the operations for finding, reading and manipulating the objects.
A key factor that influences the efficiency of an organizing task on a display, such as a computer display, is the size of a viewed space in a workspace. For a classification task on a display too small to show all of the objects, some objects are necessarily out of sight, so that a sensemaker must take additional steps and often use more time in locating and manipulating objects. In this case, search operations generally require not only scanning with the eyes, but also navigation with a pointer using panning, scrolling, and zooming. Such operations, which bring objects into the viewed space, significantly add to the time required in comparison with larger displays. The overhead of panning or scrolling can also adversely affect overall performance by distracting the sensemaker with extra steps and by requiring the sensemaker to remember things while navigating between objects.
Estes teaches that there are two primary types of models used to explain human classification behavior: exemplar-based models and rule-based models (Estes, W. K. (1994) Classification and Cognition. New York: Oxford University Press, pp 33-87). These two models of classification also correspond well with how humans organize objects into groups using a workspace such as a computer display.
In organizing objects into groups in a workspace, the rule-based classification tends to be formal. In this instance, a set of categories is determined, and explicit membership criteria are established for each of the categories by rules. To classify an object, a sensemaker checks the rules for each category and adds an object to whichever category the object satisfies the rules. Classifying an object amounts to adding the object to a “bucket” containing the objects that satisfy the membership criteria.
Determining or defining a category requires upfront work. A sensemaker may have to assign or allocate a place for the new category, determine the membership criteria, add the object to the category, and write down the membership criteria in a title or other visible label. Once this is done, however, future assignments of objects to this category go faster because the sensemaker need only check the label and then “drop” appropriate objects into the category.
With respect to computer systems, the membership criteria often take the form of a title or label on a window or folder. For example, e-mail systems, such as Eudora® and Microsoft® Outlook®, provide hierarchies of named mail folders for classifying messages. Storing an email message into an appropriate folder is an example of classifying an object. A key feature of interactive rule-based classification is that the decision about how to classify an object requires reading the membership criteria (in the form of titles or folder names), but generally does not require reading the previously classified objects (such as the email messages already in the folder).
Organizing objects into groups in a workspace using exemplar-based classification, on the other hand, is more tentative and informal. To classify an object, one must compare the object with the examples in an informal category or cluster in order to determine whether the object fits. Classifying an object amounts to placing the object in or near a cluster of similar objects.
Creating a cluster or implicit category requires less upfront work than creating a formal, or explicit category, but has greater overhead for future classifications. To set up a new cluster, a sensemaker simply places a new object in some uncrowded region of the workspace, possibly near other clusters or categories that seem somewhat related. No label or membership criteria are supplied. Future classifications are somewhat more tedious than in the case of explicit categories because the sensemaker needs to examine members of clusters in order to determine where to place new objects. Unless the sensemaker remembers tentative abstractions for a cluster, there is no shortcut for membership determination by checking a label or rule.
In a workspace, exemplar-based classification amounts to visual clustering. There are no explicit titles or rules for membership in a cluster. The boundaries of the clusters can be somewhat more tentative and ambiguous, especially when two clusters are near each other. The decision about how to classify an object requires reading or scanning other objects to detect similarity, and then locating the new object near the other objects that the new object best matches.
In interactive sensemaking workspaces, two types of overviews may be available: structural overviews that show a list of categories to which objects may be classified, and special overviews that show positional relationships of objects in the workspace.
Structural overviews are well suited for rule-based classification where the formal categories correspond to labels in an outline. FIG. 1 is an example of structural overviews. An interface, such as a drag-and-drop interface, makes the process of adding objects to a category convenient. Structural overviews can incorporate nesting, yielding hierarchical trees of categories.
However, structural overviews provide no support for exemplar-based classification because the structural overviews show the labels of formal categories, but nothing about the informal categories.
Spatial overviews provide a rendering of the workspace. Such overviews can be allocated permanently or transiently at a portion of the display space, while most of the sensemaker's work is done in a focus of the workspace. Using such spatial overviews, formal categories and informal categories can be both shown at a reduced scale.
However, because of the reduced scale, the sensemaker may have to zoom in the workspace in order or put a desired section of the workspace in focus, to recognize and understand the contents of objects for classification.
FIG. 2 shows an example of a workspace, a viewed space, an overview and objects. In FIG. 2, a focus 100 includes objects 110-130. An overview 140 provides a rendering of an entire workspace 150. A frame 160 indicates a currently viewed space within the workspace 150. An object may have one or more sub-objects within, which may form a multi-level object. A sensemaker can bring any part of the workspace into the viewed space by clicking or dragging on a region in the overview 140 or scrolling the viewed space.
U.S. Pat. No. 6,243,093 to Czerwinski et al. discloses a system for spatially organizing stored web pages that automatically highlights similar web pages during organization and retrieval tasks. When the user drags or clicks on a web page, similarity metrics between the dragged or clicked web page and other stored web pages in a single-level spatial workspace are computed and web pages with such similarity are highlighted to the sensemaker. This system computes similarity metrics between items in a spatial workspace and displays this similarity to the user. However, this system does not indicate how objects are similar, but rather simply indicates numeric scores for the similarity. Similarly, this system does not use automatic similarity indicators for labeled hierarchical organizations rather than large single level spaces.
Similar techniques have also been applied to information retrieval in large document collections, such as the Web. One such technique described by Hearst (Hearst, 1995, TileBars: Visualization of Term Distribution Information in Full Text Information Access, Proceedings of CHI '95. p. 59-66), called TileBars, creates simple colored rectangles to represent the pages in a set of documents. In this technique, the intensity of the fill color of these rectangles signifies the number of query matches on the specified page. A related technique by Woodruff et al. (Woodruff, Faulring, Rosenholtz, Morrison, & Pirolli, 2001, Using Thumbnails to Search the Web, Conference Proceedings of CHI 2001, Vol. 3, Issue 1, p. 198-205, 552) enhances standard web page thumbnails with enlarged text labels. These enlarged labels indicate the location and frequency of query terms, combining the benefits of traditional thumbnails with the benefits of simple text summaries. Nevertheless, both these techniques have been applied only to static one-dimensional documents. These techniques do not extend them to dynamic documents with multiple dimensions.